Validating power network models for monitoring and correcting operation of electric power networks

ABSTRACT

This disclosure involves verifying that a power network model corresponds to an electric power network providing electrical power in a geographical area. For instance, a validation device computes a validation score for a power network model based on a connectivity score, an asset score, and a power-flow score. The connectivity score indicates connectivity errors in the power network model as compared to the power network. The asset score indicates power-delivery errors in the power network model with respect to power-consuming assets serviced by the power network. The power-flow score indicates power-flow calculation errors in the power network model with respect to voltage ranges for the power network. The validation score is repeatedly computed for iteratively updated versions of the power network model until a threshold validation score is obtained. The validated power network model is provided to a control system for identifying and correcting errors in the power network.

CROSS-REFERENCE TO RELATED APPLICATION

This disclosure claims priority to U.S. Provisional Application No.62/353,801, entitled “Validating Electric Power Network Models,” filedJun. 23, 2016, the entirety of which is hereby incorporated by referenceherein.

TECHNICAL FIELD

This disclosure relates generally to apparatuses and processes formodeling and reproducing an electrical system to predict its performanceand thereby configure the electrical system to obtain a desiredperformance. More particularly this disclosure relates to usingvalidation scores or other quantitative measures to verify that a powernetwork model corresponds to an electric power network and is thereforeusable for controlling operation of the electrical power network.

BACKGROUND

Electric power networks, such as power grids or other power distributionsystems, are used to deliver power from power sources (e.g., powerstations and other electrical providers) through a network of switches,transformers, and other delivery infrastructure to load devices locatedin dwellings and other buildings or geographic areas. Power grid modelsor other power network models are computer representations of real powergrids. Power grid models or other power network models are used in oneor more of Advanced Grid Analytics software, power systems simulationand planning software, and in Distribution Management System (“DMS”) orEnergy Management System (“EMS”) applications used by utility operationscenters.

Various types of data can be used to generate power network models.Examples of this data include geographic information system (“GIS”)layout data, connectivity data, and device information data. The datainvolved in different aspects of power network models are typicallymanaged using human input. Thus, the data used to generate power networkmodels is prone to errors, inconsistencies, and incompleteness.

SUMMARY

Aspects and examples are disclosed for validating a power network modelby verifying that the model matches or otherwise corresponds to anelectric power network providing electrical power to multiple assetspositioned in a geographical area. For instance, a validation devicecomputes a validation score for a power network model based on aconnectivity score, an asset score, and a power-flow score. Theconnectivity score indicates connectivity errors in the power networkmodel as compared to the power network. The asset score indicatespower-delivery-attribute errors in the power network model with respectto power-consuming assets serviced by the power network. The power-flowscore indicates power-flow calculation errors in the power network modelwith respect to voltage ranges specified for the power network. Thevalidation score is repeatedly computed for iteratively updated versionof the power network model until a threshold validation score is reached(i.e., the model is validated). The validated power network model isprovided to a control system for identifying and correcting errors inthe power network.

These illustrative aspects and features are mentioned not to limit ordefine the invention, but to provide examples to aid understanding ofthe inventive concepts disclosed in this application. Other aspects,advantages, and features of the present invention will become apparentafter review of the entire application.

BRIEF DESCRIPTION OF THE FIGURES

These and other features, aspects, and advantages of the presentdisclosure are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings.

FIG. 1 is a block diagram illustrating an example of a system forvalidating power network models.

FIG. 2 is a block diagram illustrating an example of a data flow used bythe system of FIG. 1 to validate power network models.

FIG. 3 depicts an example of a user interface used by a validationapplication to display results and other output data generated byvalidating power network models.

FIG. 4 is a block diagram depicting examples of devices used to validatepower network models.

FIG. 5 is a flow chart depicting an example of a method for validating apower network model by verifying that the power network model matches anelectric power network.

FIG. 6 is a flow chart depicting an example of a method for using thevalidation application to compute a connectivity score for a powernetwork model.

FIG. 7 is a flow chart depicting an example of a method for using thevalidation application to compute a connectivity score for a powernetwork model.

DETAILED DESCRIPTION

Systems and methods are provided for validating a power network model byverifying that the model matches or otherwise corresponds to an electricpower network providing electrical power to multiple assets positionedin a geographical area. For example, a validation server defines andcomputes one or more validation scores or other metrics indicating aquality of a power network model used by a control system of an electricpower network. The validation server can calculate one or morevalidation scores indicating the quality of a power network model. Thevalidation score can be computed after performing, under a number ofassumptions, topological and quantitative (e.g., power flow) analysis ofa power grid model or other power network model. The calculatedvalidation score is used to assess both content quality and executionresults of power network models. If the power network model has beenvalidated, the power network model is more effective for diagnosing andcorrecting errors in an electric power network that corresponds to thepower network model.

Validating the power network model can include, for example, verifyingthat the power network model accurately reflects the actual elements andconnections in the corresponding electric power network. In someaspects, the validation scores indicate that planning and optimizationof a power network model can be reasonably performed. An optimized powernetwork model can be used to improve the efficiency, reliability, orother performance characteristics of an electric power network thatcorresponds to the power network model. For instance, optimizing anelectric power network can reduce outages in the network. Certain typesof optimization software cannot be properly used to optimize a powernetwork model if the power network model has a low quality. Thevalidation scores described herein can ensure that a model has asufficiently high quality level for use by optimization and analyticssoftware.

In some aspects, the power network model-validation features provided bythe analytical application can provide engineers with a validation scoreindicating whether a power network model is well-formed enough for powerflow calculations and other analytics to be performed on the powernetwork model. In additional or alternative aspects, the validationscores can allow personnel without in-depth technical knowledge ofelectric power networks (e.g., executives, sales and project managers,etc.) to understand how accurate a power network model is.

Certain aspects described herein are applied to the operation ofelectric power networks and thereby improve modeling systems, controlsystems, or both that are used to control how electric power networksare configured. In particular, certain validation systems and methodsdescribed herein improve existing control systems by improving theaccuracy with which a particular model reflects the devices,connections, and power flow in an electric power network. For example,existing systems lack any algorithm for computing separate scores thatevaluate different aspects of a power network model (e.g., connectivity,assets, and power flow). By contrast, certain implementations describedherein provide a specific validation algorithm that evaluates an overallquality of the model (e.g., the validation score) while also providingmore granular evaluations of certain features of the model that areparticular to electric power networks (e.g., connectivity, power flow,etc.). Furthermore, in conventional control systems that rely onun-validated power network models, errors in the power network model canlead to sub-optimal or improper operation of the modeled power network.Thus, in contrast to conventional control systems, a control system thatuses a validated power control model can more effectively manageelectric power networks to improve efficiency of power delivery, addresssub-optimal performance, etc.

FIG. 1 is a block diagram illustrating an example of a system thatincludes a validation server 102 for validating power network models.The system includes the validation server 102, which can execute avalidation application 104, one or more computing devices 112, a controlsystem 113, and an electric power network 116 that corresponds to thepower network model being analyzed and validated by the validationapplication 104. The validation server 102, the computing device 112,the control system 113, and various data sources such as GIS data 108and additional data 110 are communicatively coupled via a data network106.

The validation server 102 can access one or more data sources via a datanetwork 106. Examples of data sources include databases or other datastructures from which GIS data 108 and additional data 110 can beretrieved. In some embodiments, the GIS data 108, the additional data110, or both include connectivity data (e.g., nodes and edges of a powernetwork). The validation application 104 can validate, generate, modify,or otherwise use power network models based on one or more of the GISdata 108 and the additional data 110. The validation application 104 canalso validate one or more power network models that are generated ormodified based on one or more of the GIS data 108 and the additionaldata 110. The validation application 104 can configure the validationserver 102 to provide output data, which represents the results of thepower network model validation, to the computing device 112 via the datanetwork 106.

The control system 113 can be communicatively coupled to one or moredevices in the electric power network 116. The control system 113 stores(or otherwise has access to) one or more validated power network models114. The control system 113 uses a validated power network model 114 toidentify errors, inefficiencies, or other issues in the electric powernetwork 116. For example, the control system 113 may execute diagnosticsoftware or another control service, such as Advanced Grid Analyticssoftware, that uses the validated power network model 114 to developplans for optimizing the electric power network 116. The diagnosticsoftware or another control service predicts (or otherwise analyzes) thebehavior of the electric power network 116.

For instance, the electric power network 116 can include one or morepower sources 118 a-n and various sets of power-consuming assets 120a-n. The validated power network model 114 can include modeled nodescorresponding to the power sources 118 a-n and the power-consumingassets 120 a-n, as well as modeled links corresponding to connectionsamong the power sources 118 a-n and the power-consuming assets 120 a-n.The validated power network model 114 can be used to identify, forexample, a sub-optimal performance of power source 118 b in providingpower to the power-consuming assets 120 b. Based on this identification,the control system 113 can modify one or more configuration parametersof the electric power network 116.

The control system 113 can operated more effectively when using avalidated network model 114. For instance, the increased accuracy of thevalidated network model 114 can provide more reliable calculations orsimulations of actual and fictional operation conditions of the physicalpower network counterpart (i.e., the electric power network 116 that ismodeled by the validated network model 114). In one example, thevalidation process can identify incorrectly entered data in systems suchas the GIS data 108, such as power grid assets reported at an incorrectlocation, assets attributes (e.g. power and voltage ratings) incorrectlyentered, etc. In another example, a control system 113 that executesAdvanced Grid Analytics and other smart grid software can use avalidated network model to accurately identify ways to optimize powergrid operations. Examples of these optimization include options forshifting power usage away from overloaded assets, identification ofvarious usage patterns on each physical conductor in the electric grid,and recommendations for equipment upgrades and other improvements to thephysical implementation of the electric power network 116. By contrast,a power network model with validation errors would lead to erroneousoutputs by Advanced Grid Analytics software or other power grid softwareused by the control system 113, which could potentially result inhazardous power grid operating conditions.

For illustrative purposes, FIG. 1 depicts the validation application 104being executed on a validation server 102. However, otherimplementations are possible. For example, in some embodiments, avalidation application 104 as described herein can be executed on acomputing system other than a server, such as a laptop or other end-usercomputing device.

FIG. 2 is a block diagram illustrating an example of a data flow used bythe validation application 104 to validate one or more power networkmodels 202. In this example, the validation application 104 can generateor modify one or more power network models 202 based on data describingvarious elements of an electric power network. Examples of datadescribing various elements of an electric power network include the GISdata 108 and the additional data 110. The validation application 104 cancommunicate with data sources such as GIS databases or planning software(e.g., Cyme) maintained by utility providers. The validation application104 can extract relevant GIS data 108, relevant additional data 110, orboth from these data sources. The validation application 104 cangenerate or modify a database or other file that stores records and datavalues corresponding to the power network model 202.

The validation application 104 can validate the power network model 202for use by Advanced Grid Analytics software or other suitableapplications executed by a control system 113. For example, thevalidation application 104 can compute different component scores fordifferent aspects of a given power network model 202 using parameters203. The parameters 203 can include one or more adjustable outcomes,assumptions, weights, or some combination thereof. Examples of thecomponent scores include a connectivity score 204, a power-flow score208, and an asset score 206. The connectivity score 204 corresponds toconnectivity issues, and can indicate connectivity errors in the powernetwork model 202 as compared to the electric power network 116. Theasset score 206 corresponds to device issues, and can indicatepower-delivery attribute errors in the power network model 202 withrespect to power-consuming assets 120 a-n serviced by the electric powernetwork 116. The power-flow score 208 corresponds to power-flowcalculation issues, and can indicate power-flow calculation errors inthe power network model 202 with respect to voltage ranges or otherpower-flow attributes specified for the electric power network 116. Thevalidation application 104 can compute a validation score 210 for thepower network model 202 based on one or more of these component scores.

In some aspects, the validation application 104 can calculate thevarious scores after performing topological and quantitative analysis ofa power network model 202 under a number of assumptions. For example,the connectivity score 204 can be computed based on an analysis of whichelements in power network model 202 are connected to one another, asdetermined from the GIS data 108 or additional data 110. The validationapplication 104 can generate a data graph of the power network model202, where each node is a point of the electric power network and eachedge is a connection (a line, switch, etc.) between two nodes. The nodesand connections can be identified in the GIS data 108 or additional data110. The connectivity score 204 increases if, for example, fewertopological islands or connectivity islands (i.e., sets of nodes andedges that are not connected to some nodes or edges) exist in the datagraph.

The validation application 104 can also calculate a validation score 210(e.g., a global score or overall model health score) for display on aninterface 212 using some combination of the connectivity score 204, thepower-flow score 208, and the asset score 206. In some aspects, thevalidation score 210 is an overall model score that is the weighted sumof the connectivity score 204, the power-flow score 208, and the assetscore 206. The validation score 210 can indicate the quality of thepower network model 202. For example, a higher validation score 210 canindicate a higher quality of the power network model data in the powernetwork model 202.

In some aspects, the validation application 104 can include arecommendation engine 209, as depicted in the example of FIG. 2. Therecommendation engine 209 includes executable program code for analyzingthe power network model 202. The recommendation engine 209 can identify,based on this analysis, one or more potential sources of error thatcontribute to a lower validation score 210. The recommendation engine209 can generate one or more suggested modifications for addressing thepotential sources of error identified from the analysis.

In some aspects, the recommendation engine 209 can present one or morerecommendations via the interface 212 for acceptance by a user. Forinstance, a recommendation can be presented via the interface 212. Ifuser input accepting the recommendation is received via the interface212, the validation application 104 can execute a model update engine214, as indicated by the arrow from the interface 212 to the modelupdate engine 214. The model update engine 214 includes executableprogram code for modifying the power network model 202 in accordancewith one or more accepted recommendations. In other aspects, therecommendation engine 209 can cause the model update engine 214 toimplement a suggested modification without requiring an acceptance inputfrom the interface 212, as indicated by the arrow from therecommendation engine 209 to the model update engine 214. In variousaspects involving the model update engine 214, the updated power networkmodel 202 can be revalidated by computing an updated validation score210 for the power network model 202 as modified using the model updateengine 214. The updated power network model 202 can have improvedvalidation scores over a previous version of the power network model202.

The connectivity score 204, the power-flow score 208, and the assetscore 206 can be computed based on one or multiple configurable chartsstored in a non-transitory computer-readable medium and accessible tothe validation application 104. Each chart can include conditions,rules, or other schema under which points are attributed to certainfeatures of the power network model 202. In some aspects, theseconditions, rules, or other schema can involve simple record checking.In additional or alternative aspects, these conditions, rules, or otherschema can involve the use of advanced proprietary algorithms (e.g., apower flow engine, a graph processor, fuzzy logic, etc.). Scoring chartswith conditions, rules, or other schema may vary with time, customers,or versions/functionality of any software used with a power networkmodel.

Examples of Devices for Validating Power Network Models

FIG. 4 is a block diagram depicting examples of implementations fordevices used to validate power network models. The validation server 102(or other suitable computing system executing the validation application104), the computing device 112, and the control system 113 canrespectively include processors 402, 412, 424. Non-limiting examples ofthe processors 402, 412, 424 include a microprocessor, afield-programmable gate array (“FPGA”) an application-specificintegrated circuit (“ASIC”), or other suitable processing devices. Eachof the processors 402, 412, 424 can include any number of processingdevices, including one. The processors 402, 412, 424 can becommunicatively coupled to computer-readable media, such as memorydevices 404, 414, 426.

One or both of the memory devices 404, 414, 426 can store program codethat, when executed by the processors 402, 412, 424, causes a respectiveone of the processors 402, 412, 424 to perform operations describedherein. The program code may include processor-specific program codegenerated by a compiler and/or an interpreter from code written in anysuitable computer-programming language. Non-limiting examples ofsuitable computer-programming languages include C, C++, C#, VisualBasic, Java, Python, Perl, JavaScript, ActionScript, and the like.

One example of the program code is the validation application 104 storedin the memory device 404. Another example of the program code is a userapplication 420 stored in the memory device 414. When executed, the userapplication 420 can communicate with the validation application 104 anddisplay the interface 212. Another example of the program code is acontrol service 432 stored in the memory device 426. When executed, theuser application 420 can communicate with the validation application104, obtain a validated power network model, and use the validated powernetwork model to identify and correct errors and other issues withrespect to an electric power network 116.

Each of the memory devices 404, 414, 426 may include one or morenon-transitory computer-readable media such as (but not limited to) anelectronic, optical, magnetic, or other storage device capable ofproviding a processor with computer-readable instructions. Non-limitingexamples of such optical, magnetic, or other storage devices includeread-only (“ROM”) devices, random-access memory (“RAM”) devices,magnetic disks, magnetic tapes or other magnetic storage, memory chips,an ASIC, configured processors, optical storage devices, or any othermedium from which a computer processor can read program code.

In the example depicted in FIG. 4, the validation server 102, thecomputing device 112, and the control system 113 respectively includebuses 406, 416, 428. Each of the buses 406, 416, 428 can communicativelycouple one or more components of a respective one of the validationserver 102 and the computing device 112. Each of the buses 406, 416, 428can communicate input events and output events among components of thevalidation server 102, the computing device 112, and the control system113, respectively. For example, the validation server 102 can includeone or more input devices and one or more output devices, such as adisplay device 410. The computing device 112 can also include one ormore input devices and one or more output devices, such as a displaydevice 422 for displaying a graphical interface provided by thevalidation application 104 or generated using data received from thevalidation application 104. The computing device 112 can also includeone or more input devices and one or more output devices, such as adisplay device 434 for displaying a graphical interface used to controlor adjust various aspects of the electric power network 116. Inputdevices and output devices can be communicatively coupled via buses 406,416, 428. The communicative coupling can be implemented via any suitablemanner (e.g., a connection via a printed circuit board, connection via acable, communication via wireless transmissions, etc.). Non-limitingexamples of display devices 410, 422, 434 include an LCD screen, anexternal monitor, a speaker, or any other device that can be used todisplay or otherwise present outputs generated by a validation server102, a user application 420, or a control service 432.

Although the processors 402, 412, 424, the memory devices 404, 414, 426,and the buses 406, 416, 428 are respectively depicted in FIG. 4 asseparate components in communication with one another, otherimplementations are possible. For example, the processors 402, 412, 424,the memory devices 404, 414, 426, and the buses 406, 416, 428 can berespective components of respective printed circuit boards or othersuitable devices that can be included in one or more of the validationserver 102, the computing device 112, and the control system 113.

The validation server 102 and the computing device 112 can also includerespective communication interfaces 408, 418, 430. Each of thecommunication interfaces 408, 418, 430 includes any device or group ofdevices suitable for establishing a wired or wireless data connection toone or more data networks. Non-limiting examples of the communicationinterfaces 408, 418, 430 include Ethernet network adapters, modems, etc.

Examples of Computing a Validation Score

FIG. 5 is a flow chart depicting an example of a method 500 for usingthe validation application 104 to validate a power network model 202 byverifying that the power network model 202 matches an electric powernetwork 116. For illustrative purposes, the method 500 is described withreference to one or more of the examples described above with respect toFIGS. 1-4. Other implementations, however, are possible.

The method 500 involves accessing the power network model, wherein thepower network model is generated from data stored in a geographicinformation system database and describing assets and power stations ofthe electric power network, as shown in block 502. One or more suitableprocessing devices 402 of a validation server 102 can implement block502 by executing program code that includes the validation application104. In some aspects, the executed validation application 104 configuresthe validation server 102 to retrieve the power network model 202 from anon-transitory computer-readable medium, such as the memory device 404of the validation server 102. In other aspects, the executed validationapplication 104 configures the validation server 102 to receive thepower network model 202 from a non-transitory computer-readable medium,such as the memory device 414 of the computing device 112, viacommunications over a data network 106. In other aspects, the executedvalidation application 104 configures the validation server 102 toreceive the power network model 202 from a non-transitorycomputer-readable medium, such as the memory device 426 of the controlsystem 113, via communications over the data network 106.

The method 500 also involves computing a validation score for the powernetwork model from a combination of a connectivity score, an assetscore, and a power flow score, as shown in block 504. One or moresuitable processing devices 402 of a validation server 102 can implementblock 504 by executing program code that includes the validationapplication 104. The executed validation application 104 configures thevalidation server 102 to compute each of the component scores (i.e., theconnectivity score 204, the power-flow score 208, the asset score 206).Examples of computing some of these scores are described herein withrespect to FIGS. 6 and 7. The validation application 104 computes thevalidation score 210 from a combination of these component scores.

The validation application 104 can use any suitable scale (e.g., apercentage) for the validation score 210. The validation application 104can compute the validation score 210 as a weighted sum of theconnectivity score 204, the power-flow score 208, the asset score 206,and any other scores that may be used. A maximum score can be achievablein each category. A maximum global score can also be achievable.

In one example, a validation score S (e.g., an overall “health score”)for a power network model can be defined as:

S=w _(C) S _(c) +w _(A) S _(A) w _(P) S _(P).  (1)

In equation 1, the connectivity weight w_(C) represents the weightapplied to the connectivity score S_(c) in the computation of the globalscore. The asset weight w_(A) represents the weight applied to the assetscore S_(A) in the computation of the global score. The power flowweight w_(P) represents the weight applied to the power-flow score S_(P)in the computation of the global score.

In one example, the validation application 104 can compute or otherwiseobtain, for a particular power network model, a connectivity score ofS_(c)=85%, an asset score of S_(A)=72%, and a power-flow score ofS_(P)=940 out of 1200. The validation application 104 can retrieveweights for these scores from a non-transitory computer-readable medium.Examples of the weights include a connectivity weight w_(C)=5, an assetweight w_(A)=8, and a power flow weight w_(P)=1. The power network modelhealth score S computed with equation 1 is S=5×85+8×72+940=1941, out ofa maximum score of 2500.

In various aspects, each component score (i.e., connectivity score,asset score, and power-flow score) and the power network model healthscore are defined to meet the specific criteria for a particular model.These criteria can be provided by data consulting services, utilityengineers, or other suitable personnel and stored in a non-transitorycomputer-readable medium. Examples of the criteria include the weightsdescribed above and extreme values (i.e., minimum values, maximumvalues, or both) achievable for each type of score.

The method 500 also involves determining whether the validation score isbelow a threshold validation score, as shown in block 506. One or moresuitable processing devices 402 of a validation server 102 can implementblock 506 by executing program code that includes the validationapplication 104. The executed validation application 104 configures thevalidation server 102 to retrieve the threshold validation score from anon-transitory computer-readable medium, such as the memory 404 of thevalidation server 102. The validation application 104 can compare thethreshold validation score with the validation score computed at block504.

The validation server 102 can obtain the threshold validation score inany suitable manner. In some aspects, the validation server 102 canreceive input (e.g., via a graphical interface for accessing thevalidation application 104) from a computing device 112 via a datanetwork 106. The input can include a user-specified threshold validationscore for the power network model 202. The threshold validation scorecan be obtained from (or based on) specifications provided from anexpert or other suitable user. An example of a threshold validationscore is a minimum score indicating whether the power network model 202is well-formed enough to be used for performing power flow calculationsor other analyses on the power network model 202.

If the validation score is below a threshold validation score, themethod 500 proceeds to block 508, which involves accessing an updatedversion of the power network model. One or more suitable processingdevices 402 of a validation server 102 can implement block 508 byexecuting program code that includes the validation application 104. Theexecuted validation application 104 configures the validation server 102to access an updated version of the power network model 202. In someaspects, the updated version of the power network model 202 is createdbased on one or more modifications to the power network model 202, wherethe modifications are specified via one or more users received at thecomputing device 112 and transmitted to the validation server 102. Inadditional or alternative aspects, the updated version of the powernetwork model 202 is created based on one or more modifications to thepower network model 202, where the modifications are generated at leastin part based on the validation application 104 identifying one or morepotential solutions to errors in the power network model 104.

For instance, the validation application 104 can generate and output arecommendation for modifying the power network model. The modificationcan be recommended to resolve one or more modeling errors indicated bythe validation score 210 or by one or more of the component scores. Asan example, the validation application 104 can determine that aparticular component score (e.g., the connectivity score 204) is moreheavily weighted than one or more other component scores. The validationapplication 104 can analyze the power network model 202 to identify oneor more issues that resulted in the heavily weighted component score(e.g., connectivity issues, such as an excessive number of islands). Thevalidation application 104 can generate a recommended solution to theidentified issues. Examples of recommended solutions include changingcertain asset attributes (e.g., one or more attribute values affectingpower flow scores), moving assets within the power network model 202,adding assets to the power network model 202, deleting assets from thepower network model 202, and changing connections among assets withinthe power network model 202 (e.g., connecting assets to assets otherthan assets originally connected).

The validation application 104 can output the recommendation by, forexample, displaying the recommendation on a graphical interface at thedisplay device 410 or transmitting the recommendation to a computingdevice 112 for display on a graphical interface at the display device422. The graphical interface may include an option for a user to acceptthe recommendation. If the validation application 104 receives a userinput indicating acceptance of the recommendation, the validationapplication 104 (or another application in communication with thevalidation application 104) can modify the power network model 202 inaccordance with the recommendation. The modified power network model 202can be accessed at block 508 of the method 500.

In another example, the validation application 104 (or anotherapplication in communication with the validation application 104) canautomatically apply one or more modifications without receiving userinput (e.g., input that selects a modification or accepts arecommendation regarding a modification). For instance, if a potentialsolution to a model error is identified, the validation server 102 canimplement the solution automatically and generate an updated version ofthe power network model 202. The modified power network model 202 can beaccessed at block 508 of the method 500.

If the validation score is greater than or equal to the thresholdvalidation score, the method 500 proceeds to block 510, which involvesoutputting an indicator that the validated power network model issuitable for use by a control system 113. For example the indicator,which can be transmitted to a control system 113 or presented via theuser interface 212, can designate the validated power network model 202as suitable for further use by Advanced Grid Analytics software, DMD,EMS, or other power grid software that may be executed by a controlsystem 113. Examples of the indicator include the validation scoreitself, a color-coded indicator (e.g., a green highlight or text for thevalidation score), or a message stating that the power network model 202is error-free or has otherwise passed validation.

In some aspects, the validation server 102 can output the validatedpower network model to a control system 112 that identifies and correctserrors in the electric power network. One or more suitable processingdevices 402 of a validation server 102 can implement block 510 byexecuting program code that includes the validation application 104. Theexecuted validation application 104 configures the validation server 102to output the power network model 202. For example, the validationserver 102 can transmit the power network model 202 to the controlsystem 113, which can store the validated power network model 202 in thememory 426.

The control service 432 can access the validated power network model 202to identify one or more errors in the operation of the electric powernetwork 116. The control service 432 can correct the identified errorsby, for example, changing a configuration of the electric power network116. Using a validated network model allows the control system 113,which executes Advanced Grid Analytics and other smart grid software, toaccurately identify ways to optimize power grid operations, such asidentifying ways to shift power usage away from overloaded assets,reporting various usage patterns on each physical conductor in theelectric grid, and recommending equipment upgrades and other equipmentchanges.

Connectivity Score Example

FIG. 6 is a flow chart depicting an example of a method 600 for usingthe validation application 104 to compute a connectivity score for apower network model 202. For illustrative purposes, the method 600 isdescribed with reference to one or more of the examples described abovewith respect to FIGS. 1-5. Other implementations, however, are possible.

The method 600 involves detecting connections among modeled nodes in thepower network model and a modeled power source in the power networkmodel, as shown in block 602. One or more suitable processing devices402 of a validation server 102 can implement block 602 by executingprogram code that includes the validation application 104. The executedvalidation application 104 configures the validation server 102 toaccess the power network model 202, the GIS data 108, the additionaldata 110, or some combination thereof. The validation application 104further configures the validation server 102 to determine, based on thisaccessed data, which elements in the power network model 202 areconnected to one another.

The method 600 also involves generating a graph data structurerepresenting the power network modeled nodes and the connection, asshown in block 604. One or more suitable processing devices 402 of avalidation server 102 can implement block 604 by executing program codethat includes the validation application 104. The executed validationapplication 104 configures the validation server 102 to generate a datagraph of the power network model 202. The generated data graph includesgraph nodes representing modeled nodes from the power network model 202(e.g., nodes representing points in the electric power network 116, suchas power sources 118 a-n, power consuming devices 120 a-n, or both). Inthe data graph, each edge between graph nodes represents a connection (aline, switch, etc.) between two modeled nodes from the power networkmodel 202. The nodes and connections can be identified from the GIS data108 or additional data 110. The processing device 402 can store thegraph data structure in a suitable non-transitory computer-readablemedium, such as the memory 404.

The method 600 also involves detecting, from the connections representedin the graph data structure, de-energized nodes in the power networkmodel that lack connectivity to the power network modeled power source,as shown in block 606. One or more suitable processing devices 402 of avalidation server 102 can implement block 606 by executing program codethat includes the validation application 104. The executed validationapplication 104 configures the validation server 102 to detect thede-energized nodes (e.g., connectivity islands). The validationapplication 104 can detect the de-energized nodes based on determiningthat some of the nodes lack connectivity to a power source in the powernetwork model 202.

The method 600 also involves calculating the connectivity score as afunction of a number of the nodes and a number of the de-energizednodes, as shown in block 608. One or more suitable processing devices402 of a validation server 102 can implement block 608 by executingprogram code that includes the validation application 104. The executedvalidation application 104 configures the validation server 102 tocompute the connectivity score. An increase in the number of thede-energized nodes causes the connectivity score to indicate increasedconnectivity errors (e.g., by decreasing the connectivity score computedby the function).

The connectivity score 204 can be computed based on various attributesreflective of connectivity in a modeled electric power network. Examplesof attributes used to compute the connectivity score 204 include anumber of topological islands, a number of consumers, a number oftransformers, the total installed power, a number of energized islands,a number of de-energized islands, a number of nodes on energized orde-energized islands, a number of energized or de-energized consumers, adistribution of transformers, installed power in energized andde-energized islands, a size and installed power of the largestenergized and de-energized islands, installed generation capacity, andavailability of island fixes that can be determined or appliedautomatically. The validation application 104 can compute theconnectivity score 204 from some or all of these attributes.

In one example, the validation application 104 computes a connectivityscore S_(e) using the following formula:

$\begin{matrix}{S_{c} = {\frac{n_{node} - n_{u} - {n_{z}/2}}{n_{node}}.}} & (2)\end{matrix}$

In equation 2, the term n_(node) represents a number of nodes (or buses)in the power network model 202 being validated. The term n_(u)represents a number of nodes that are not energized. The term n_(z)represents a number of nodes that are not energized and that are onislands without consumers. In this example, equation 2 emphasizes thenumber of connected nodes. For instance, a larger number ofnon-energized nodes results in a lower connectivity score 204. Equation2 emphasizes customer connectivity. For instance, the number of nodes innon-energized islands with customers causes a greater decrease in theconnectivity score 204 as compared to the number of nodes innon-energized islands without customers.

In one example, the validation application 104 accesses a model of apower distribution network with n_(node)=10000 nodes. The validationapplication 104 computes a graph representing interconnections amongthese nodes. The validation application 104 analyzes the graph andthereby determines that n_(u)=1500 nodes are not energized. Thevalidation application 104 also determines, from the graph analysis,that of the non-energized nodes are on islands with no consumers(n_(z)=500). The validation application 104 uses these values andequation 2 to compute a connectivity percentage score

$S_{c} = {\frac{10000 - 1500 - {500/2}}{10000} = {82.5{\%.}}}$

Asset Score Example

FIG. 7 is a flow chart depicting an example of a method 700 for usingthe validation application 104 to compute a connectivity score for apower network model 202. For illustrative purposes, the method 1000 isdescribed with reference to one or more of the examples described abovewith respect to FIGS. 1-6. Other implementations, however, are possible.

The method 700 involves identifying modeled assets included in the powernetwork model, as shown in block 702. One or more suitable processingdevices 402 of a validation server 102 can implement block 702 byexecuting program code that includes the validation application 104. Theexecuted validation application 104 configures the validation server 102to identify the power network modeled assets from the power networkmodel 202.

The method 700 also involves determining that a subset of the powernetwork modeled assets are deficient assets having power-deliveryattribute values that indicate one or more power-delivery errors, asshown in block 704. One or more suitable processing devices 402 of avalidation server 102 can implement block 704 by executing program codethat includes the validation application 104. The executed validationapplication 104 configures the validation server 102 to identify whichof the power network modeled assets has power-delivery attribute valuesindicating power-deliver errors and to identify those modeled assets asdeficient assets.

Various attributes of the devices modeled in the electric power network116 can be used to determine power-delivery errors. Examples of theseattributes and corresponding power-delivery errors include incorrectnode and winding voltage and power ratings, phase mismatches, missingmodel data, invalid connectivity loops, and switch states that causeconnectivity issues. The validation application 104 can compute theasset score 206 based on some or all of these attributes.

The method 700 also involves calculating the asset score as a functionof a number of the power network modeled assets and a number of thedeficient assets, as shown in block 706. One or more suitable processingdevices 402 of a validation server 102 can implement block 706 byexecuting program code that includes the validation application 104. Theexecuted validation application 104 configures the validation server 102to access the function from a suitable non-transitory computer-readablemedium and apply the function to the power network modeled assets andthe deficient assets. An increase in the number of the deficient assetscauses the asset score to indicate increased power-delivery errors(e.g., by decreasing the asset score computed by the function).

In one example, the validation application 104 computes an asset scoreS_(A) using the following formula.

$\begin{matrix}{S_{A} = {\frac{n_{asset} - {a_{w}n_{w}} - {a_{r}n_{r}} - {a_{p}n_{p}}}{n_{asset}}.}} & (3)\end{matrix}$

In equation 3, the term n_(asset) represents a number of assets in thepower network model under consideration. The term n_(w) represents anumber of assets having incorrect voltage data. The term a_(w)represents a weight applied to the number of assets having incorrectvoltage data. The term n_(r) represents a number of assets that aremissing a rated kVA attribute. The term a_(r) represents a weightapplied to the number of assets that are missing a rated kVA attribute.The term n_(p) represents a number of assets that are on the wrongphase. The term a_(p) represents a weight applied to the number ofassets that are on the wrong phase.

Equation 3 emphasizes the number of issues affecting each device (orasset) in the power distribution network. The weights applied to thevarious attributes indicate that different issues have different levelsof importance for a specific smart grid application. In equation 3, anasset being affected by multiple issues results in a lower asset scorethan an asset being affected by only one issue.

In one example, the validation application 104 accesses a model of apower distribution network with n_(asset)=1000 assets. The validationapplication 104 retrieves the GIS data 108 from a non-transitorycomputer-readable medium and thereby identifies various attribute valuesfor the power network modeled power distribution network. For instance,the validation application 104 determines that 100 assets have incorrectvoltage data (n_(w)=100, weight a_(w)=0.3), 50 assets are missing arated kVA attribute (n_(r)=50, weight a_(r)=0.2), and 150 assets are onthe wrong phase (n_(p)=150, weight a_(p)=0.35). The validationapplication 104 computes the asset score S_(A) from these values, andthereby determines that the asset score

$S_{A} = {\frac{1000 - {0.3 \times 100} - {0.2 \times 50} - {0.35 \times 150}}{1000} = {90.75{\%.}}}$

Power-Flow Score Example

A simplified example is provided below with respect to the power-flowscore 208. The power-flow score 208 can indicate whether the power flowcalculations for the power network model 202 are providing reasonableresults (e.g., whether the calculations are within a voltage rangeindicated by the rules, assumption, or outcomes referenced by thevalidation application 104). Non-convergence of power flow calculations(i.e. calculations that do not complete in a certain amount ofiterations) or other outcomes of power flow calculations (e.g., voltagetoo low in one location, voltage too high in another location, etc.) canindicate that the power network model 202 is incorrect. The validationapplication 104 can perform a similar process to compute a connectivityscore 204 and an asset score 206 of a power network model 202.

Examples of components used to compute the power-flow score 208 includevoltage levels, current levels, power levels (active, reactive,apparent), power factor, angles between phases, and balance/imbalancebetween phases. The validation application 104 can compute thepower-flow score 208 from some or all of these components.

A validation application 104 can perform power flow calculations for agiven power network model 202. Computing the power flow can includecomputing voltages (e.g., magnitude and angle), currents, power (e.g.,active, reactive, and apparent) at each node and phase of a powernetwork model 202. These calculations can be performed using any powerflow software.

In the example indicated in Table 1, the validation application 104 usesvoltages for power flow calculations. Voltages between 95% and 105% ofnominal voltage (commonly accepted normal voltage boundaries) can resultin the maximum score for power flow calculations. If the calculations donot converge/complete, the power-flow score 208 is zero.

TABLE 1 Outcome for each power flow run Points Does not complete orconverge 0 Voltages outside of 85%-115% of nominal 2 Voltages in95%-105% of nominal 5 (Best)

A more generalized example is provided in Table 2, provided below.Implementation-specific logic can be used to determine which outcomeapplies.

TABLE 2 Outcome for each power flow run Points Outcome 1 C₁ Outcome 2 C₂. . . . . . Outcome n C_(n)

The validation application 104 or other suitable software can performpower flow calculations using multiple assumptions around one ormultiple parameters in the power network model 202. The validationapplication 104 or other suitable software can assign, to eachcombination of assumptions, a respective weight that indicates theimportance and likeliness of occurrence. One or more weights can bestored in a non-transitory computer-readable medium and provided by, forexample, an entity from which the GIS data 108 or the additional data110 is received. Higher weights can indicate the most expectedassumptions or starting conditions. For instance, the table belowprovides an example of certain assumptions and associated weights:

TABLE 3 Distributed Station generators? voltage? Weight No 100% 4 No105% 2 Yes 100% 6 Yes 105% 4

In the simplified example below, the maximum score possible by summingthe weighted points (points multiplied by weight) for each startingcondition is 80 ((4+2+6+4)×5). A more generalized chart ofassumptions/weights can be created as follows:

TABLE 4 ID Assumption 1 . . . Assumption q Weight 1 a₁ _(—) ₁ a_(q) _(—)₁ w₁ 2 a₁ _(—) ₂ a_(q) _(—) ₂ w₂ . . . . . . . . . . . . P a₁ _(—) _(p)a_(q) _(—) _(p) w_(p)

Similar tables can be populated and used for connectivity scores 204 andasset scores 206. In some aspects, additional or alternative scores canbe used.

Example of Using Scores to Compare Network Models to Each Other

In some aspects, complex power network models are partitioned intosmaller power network models (i.e., sub-models). Partitioning a powernetwork model into sub-models increases the efficiency with which thevalidation application 104 can analyze the power network model underconsideration (e.g., by simplifying the relevant power flowcalculations). Partitioning a power network model into sub-models canalso increase the speed and precision with which the validationapplication 104 can identify a portion of the power network model thatis presenting issues interfering with validation.

For instance, the validation application 104 can partition a powernetwork model into sub-models based on substations included within thepower network modeled electric power network. In a simplified example,the electric power network can include a first substation, a first setof assets that are powered by the first substation, a second substation,and a second set of assets that are powered by the second substation.The validation application 104 partitions the corresponding powernetwork model into a first substation model, which includes the firstsubstation and the first set of assets, and a second substation model,which includes the second substation and the second set of assets. Ineach substation model, the substation acts as the power source for thesub-model. One or more feeders can be powered by the substation. In thepartitioning process, the validation application 104 excludes assetsthat are not powered by a particular substation from that substation'ssub-model.

In some aspects, certain power assets may, under normal operatingconditions, simultaneously receive power from two substations. To modelthe interaction between the two substations, the validation application104 can create a single power distribution sub-model that includes thetwo substations instead of one substation. Assets in the sub-model aretreated as being powered by either substation.

The validation application 104 can compute, for each substation model, arespective connectivity score, a respective asset score, a respectivepower-flow score, and a respective global score. For instance, in anexample involving an electric power network with four substations, thevalidation application 104 can compute the scores in Table 5 usingequations 1-3.

TABLE 5 Asset Score Sub- Connectivity S_(A) Power-flow Global modelScore S_(c) (Max: score S_(P) Score number Substation (Max: 100%) 100%)(Max: 1200) (Max: 2500) 1 A 85% 85% 900 2005 2 B 90% 80% 1025 2115 3 C100% 70% 940 2000 4 D 70% 90% 1150 2220In the example depicted in Table 5, the sub-model 4 has the highestoverall score (2220), despite having the lowest connectivity score(70%).

The validation application 104 can use the different scores to comparethe quality (or health) of the sub-models for different portions of theelectric power network under consideration (e.g., an electric powergrid). In some aspects, the validation application 104 can havedifferent modes of operation for analyzing the power network model atdifferent levels of granularity. For instance, the validationapplication 104 can operate in a global configuration mode in which thecomparison is performed at the global level. The validation application104 can operate in one or more additional configuration modes foranalyzing connectivity, asset attributes, power flow, or otherattributes of the power network model. For instance, the validationapplication 104 can be switched to the global configuration mode toanalyze overall data quality. Alternatively, the validation application104 can be switched to a connectivity configuration mode to analyzemodel connectivity using one or more connectivity scores.

In some aspects, the validation application 104 can have a trendconfiguration mode. The trend configuration mode can be used to comparedifferent versions of the same portion of an electric power network overtime. For instance, the validation application 104 can store variousscores (e.g., one or more of the global score, connectivity score, assetscore, etc.) in a database or other data structure. Each score can bestored along with a timestamp or other time indicator identifying a timeat which the particular version of the power network model was used. Inthe trend configuration mode, the validation application 104 canretrieve various scores and their corresponding time identifiers. Thevalidation application 104 can display, via a graphical interface, oneor more retrieved score types (e.g., various connectivity scores over acertain time period, various asset scores over a certain time period,etc.). This display allows for comparing scores over time, which allowsfor determining variations in data quality and evaluating theconsistency of the underlying data or data translation operations.

The defined scores allow for ranking different sub-models of an electricpower network in terms of data quality or data health. This rankingallows decision-makers to monitor data quality in their enterprisesystems in a meaningful way.

The validation application 104 can provide a user interface 212 forinteractively launching, updating, displaying, or otherwise using thecalculation of the validation scores described above and displaying theresults to a user. The validation application 104 uses the interface 212to display the global score for each power network model 202. In someaspects, the validation application 104 can also display intermediatescores in each category. The validation application 104 provides theuser interface 212 for comparing scores between multiple models as well.FIG. 3 depicts an example of the interface 212 used by a validationapplication 104 to display results and other output data generated byvalidating power network models.

Using the same power flow calculation software and topological analysissoftware, the validation application 104 can execute one or morealgorithms that provide recommendations to improve one or more of thescores calculated for a given power network model. For instance, thevalidation application 104 can detect errors in calculated voltages thatcontribute to a reduced score. The validation application 104 candisplay possible reasons for the voltage errors, such as incorrecttransformer ratios, excessive impedances or reactive power supply, etc.As another example, the validation application 104 can detecttopological errors or issues, such as nodes that are within a thresholdproximity of one another, phase mismatches, etc. The validationapplication 104 can recommend solutions for addressing these topologicalerrors or issues, such as merging nodes within a threshold proximity,correcting phases, etc.

The recommendations generated by the validation application 104 can beimplemented in any suitable manner. In some aspects, the validationapplication 104 (or another application in communication with thevalidation application 104) can automatically apply theserecommendations without user supervision or intervention. For example,the validation application 104 can access relevant model data from anon-transitory computer-readable medium and update the power networkmodel data in accordance with the recommendations. In additional oralternative aspects, the validation application 104 can output therecommendations to a user via a graphical interface. If the validationapplication 104 receives a selection of a particular recommendation viathe graphical interface (i.e., a command from a user who has decided toact on the recommendation) the validation application 104 can accessrelevant model data from a non-transitory computer-readable medium andupdate the power network model data in accordance with the selectedrecommendation.

If one or more recommendations are implemented, the validationapplication 104 (or another application in communication with thevalidation application 104) generates an updated version of the powernetwork model. The updated version of the power network model includesone or more changes corresponding to the implemented recommendations.The validation application 104 computes one or more new scores (e.g., aglobal score or one or more component scores) for the updated version ofthe power network model. The updated scores can be computed in themanner described above. Based on model corrections adopted by the user,the score for the updated version of the power network model could bebetter than a prior score for a prior version of the power networkmodel.

The user can repeat this process until one or more desired scores havebeen obtained. Responsive to one or more user commands, the validationapplication 104 can export, save, or otherwise output the version of thepower network model having the satisfactory score. For example, theoutputted version of the power network model can be stored in anon-transitory computer-readable medium that is accessible to othersystems, such as computing systems that execute Advanced Grid Analyticssoftware, power systems simulation and planning software, andDistribution Management System (“DMS”) or Energy Management System(“EMS”) applications used by utility operations centers.

General Considerations

Numerous specific details are set forth herein to provide a thoroughunderstanding of the claimed subject matter. However, those skilled inthe art will understand that the claimed subject matter may be practicedwithout these specific details. In other instances, methods,apparatuses, or systems that would be known by one of ordinary skillhave not been described in detail so as not to obscure claimed subjectmatter.

The features discussed herein are not limited to any particular hardwarearchitecture or configuration. A computing device can include anysuitable arrangement of components that provide a result conditioned onone or more inputs. Suitable computing devices include multipurposemicroprocessor-based computer systems accessing stored software thatprograms or configures the computing system from a general-purposecomputing apparatus to a specialized computing apparatus implementingone or more aspects of the present subject matter. Any suitableprogramming, scripting, or other type of language or combinations oflanguages may be used to implement the teachings contained herein insoftware to be used in programming or configuring a computing device.

Some portions are presented in terms of algorithms or symbolicrepresentations of operations on data bits or binary digital signalsstored within a computing system memory, such as a computer memory.These algorithmic descriptions or representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Analgorithm is a self-consistent sequence of operations or similarprocessing leading to a desired result. In this context, operations orprocessing involves physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared, or otherwise manipulated. It has proven convenientat times, principally for reasons of common usage, to refer to suchsignals as bits, data, values, elements, symbols, characters, terms,numbers, numerals, or the like. It should be understood, however, thatall of these, and similar terms, are to be associated with appropriatephysical quantities and are merely convenient labels. Unlessspecifically stated otherwise, it is appreciated that throughout thisspecification, discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” and “identifying” or the likerefer to actions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulates or transforms data represented as physical electronic ormagnetic quantities within memories, registers, or other storagedevices, transmission devices, or display devices of the computingplatform.

The system or systems discussed herein are not limited to any particularhardware architecture or configuration. A computing device can includeany suitable arrangement of components that provide a result conditionedon one or more function calls. Suitable computing devices includemultipurpose microprocessor-based computer systems accessing storedsoftware that programs or configures the computing system from ageneral-purpose computing apparatus to a specialized computing apparatusimplementing one or more aspects of the present subject matter. Anysuitable programming, scripting, or other type of language orcombinations of languages may be used to implement the teachingscontained herein in software to be used in programming or configuring acomputing device.

Aspects of the methods disclosed herein may be performed in theoperation of such computing devices. The order of the blocks presentedin the examples above can be varied; for example, blocks can bere-ordered, combined, and/or broken into sub-blocks. Certain blocks orprocesses can be performed in parallel.

The use of “adapted to” or “configured to” herein is meant as open andinclusive language that does not foreclose devices adapted to orconfigured to perform additional tasks or steps. Additionally, the useof “based on” is meant to be open and inclusive, in that a process,step, calculation, or other action “based on” one or more recitedconditions or values may, in practice, be based on additional conditionsor values beyond those recited. Headings, lists, and numbering includedherein are for ease of explanation only and are not meant to belimiting.

While the present subject matter has been described in detail withrespect to specific aspects thereof, it will be appreciated that thoseskilled in the art, upon attaining an understanding of the foregoing,may readily produce alterations to, variations of, and equivalents tosuch aspects. Accordingly, it should be understood that the presentdisclosure has been presented for purposes of example rather thanlimitation and does not preclude inclusion of such modifications,variations, and/or additions to the present subject matter as would bereadily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A method for verifying that a power network modelmatches an electric power network providing electrical power to multipleassets positioned in multiple locations in a geographical area, themethod comprising: accessing, by a validation device, the power networkmodel, wherein the power network model is generated from asset datastored in a geographic information system database and describing assetsand power stations of the electric power network; computing, by thevalidation server, a validation score for the power network model byperforming operations comprising: (a) computing a connectivity scoreindicating connectivity errors in the power network model as compared tothe electric power network, wherein computing the connectivity scorecomprises: (i) detecting connections among modeled nodes in the powernetwork model and a modeled power source in the power network model,(ii) generating, in a non-transitory computer-readable medium, a graphdata structure representing the modeled nodes and the connection, (iii)detecting, from the connections represented in the graph data structure,de-energized nodes in the power network model that lack connectivity tothe modeled power source, and (iv) calculating the connectivity score asa function of a number of the nodes and a number of the de-energizednodes, wherein an increase in the number of the de-energized nodescauses the connectivity score to indicate increased connectivity errors,(b) computing an asset score indicating power-delivery attribute errorsin the power network model with respect to power-consuming assetsserviced by the electric power network, wherein computing the assetscore comprises: (i) identifying modeled assets included in the powernetwork model, (ii) determining that deficient assets, which are asubset of the modeled assets, have power-delivery attribute valuesindicating a power-delivery error, and (iii) calculating the asset scoreas a function of a number of the modeled assets and a number of thedeficient assets, wherein an increase in the number of the deficientassets causes the asset score to indicate increased power-deliveryerrors, (c) computing a power-flow score indicating power-flowcalculation errors in the power network model with respect to voltageranges specified for the electric power network, and (d) calculating thevalidation score based on the connectivity score, the asset score, andthe power-flow score; determining, by the validation server, that thevalidation score is below a threshold validation score; computing, bythe validation server, an updated validation score subsequent to amodification in the power network model, wherein the modificationchanges one or more of (i) the connections in the power network model,(ii) the power-delivery attribute values in the power network model, and(iii) power-flow calculations in the power network model; and causing,by the validation server, a control system to identify and correcterrors in the electric power network using the power network model withthe modification, wherein the validation device outputs the powernetwork model based on the updated validation score exceeding thethreshold validation score.
 2. The method of claim 1, wherein thefunction of the number of the nodes and the number of the de-energizednodes computes a percentage of the nodes that have connectivity to themodeled power source.
 3. The method of claim 1, wherein computing theconnectivity score further comprises and identifying, from the powernetwork model, unoccupied de-energized nodes indicating locations thatare serviced by the electric power network and that are modeled aslacking consumers, wherein the connectivity score is also calculatedfrom a number of unoccupied de-energized nodes, wherein an increase inthe number of the unoccupied de-energized nodes causes the connectivityscore to indicate increased connectivity errors.
 4. The method of claim1, wherein the function of the number of the modeled assets and thenumber of the deficient assets computes a percentage of the modeledassets that lack the power-delivery errors.
 5. The method of claim 1,further comprising: partitioning, by the validation device and prior tocomputing the validation score, the power network model into a sub-modelhaving the modeled power source and an additional sub-model having anadditional modeled power source, wherein the validation score iscomputed for the sub-model; computing, by the validation server, anadditional validation score for the additional sub-model, wherein theadditional validation score exceeds the threshold validation score; anddetermining, by the validation server, that a modeling error in thepower network model is caused by the sub-model rather than theadditional sub-model based on the additional validation score exceedingthe threshold validation score, wherein the modification to the powernetwork model is applied to the sub-model.
 6. The method of claim 5,further comprising: outputting, by the validation server, arecommendation for modifying the power network model to resolve themodeling error; and applying, by the validation server, the modificationresponsive to receiving input indicating acceptance of therecommendation.
 7. The method of claim 5, further comprising:identifying, by the validation server, the modification for resolvingthe modeling error; and automatically applying, by the validationserver, the modification without receiving user input indicative ofselecting the modification.
 8. A system comprising: an electric powernetwork configured for providing electrical power to multiple assetspositioned in multiple locations in a geographical area; a controlsystem communicatively coupled to the electric power network; and avalidation server communicatively coupled to the control system, thevalidation server configured for: computing a validation score for apower network model by performing operations comprising: computing aconnectivity score indicating connectivity errors in the power networkmodel as compared to the electric power network computing an asset scoreindicating power-delivery errors in the power network model with respectto power-consuming assets serviced by the electric power network,computing a power-flow score indicating power-flow calculation errors inthe power network model with respect to voltage ranges specified for theelectric power network, and calculating the validation score from theconnectivity score, the asset score, and the power-flow score;determining that the validation score is below a threshold validationscore; computing an updated validation score subsequent to amodification in the power network model, wherein the modificationchanges one or more of (i) connections in the power network model, (ii)power-delivery attribute values in the power network model, and (iii)power-flow calculations in the power network model, and transmitting thepower network model with the modification to the control system, whereinthe power network model with the modification is outputted based on theupdated validation score exceeding the threshold validation scorewherein the control system configured for: receiving the power networkmodel from the validation server, identifying, from the power networkmodel, errors in the electric power network, and correcting theidentified errors by changing a configuration of the electric powernetwork.
 9. The system of claim 8, wherein computing the connectivityscore comprises: detecting connections among modeled nodes in the powernetwork model and a modeled power source in the power network model;generating, in a non-transitory computer-readable medium, a graph datastructure representing the modeled nodes and the connection; detecting,from the connections represented in the graph data structure,de-energized nodes in the power network model that lack connectivity tothe modeled power source; and calculating the connectivity score as afunction of a number of the nodes and a number of the de-energizednodes, wherein an increase in the number of the de-energized nodescauses the connectivity score to indicate increased connectivity errors.10. The system of claim 9, wherein the function of the number of thenodes and the number of the de-energized nodes computes a percentage ofthe nodes that have connectivity to the modeled power source.
 11. Thesystem of claim 10, wherein computing the asset score comprises:identifying modeled assets included in the power network model;determining that deficient assets, which are a subset of the modeledassets, have power-delivery attribute values indicating a power-deliveryerror; and calculating the asset score as a function of a number of themodeled assets and a number of the deficient assets, wherein an increasein the number of the deficient assets causes the asset score to indicateincreased power-delivery errors.
 12. The system of claim 11, wherein thefunction of the number of the modeled assets and the number of thedeficient assets computes a percentage of the modeled assets that lackthe power-delivery errors.
 13. The system of claim 8, wherein thevalidation server is further configured for: partitioning, prior tocomputing the validation score, the power network model into a firstsub-model having a first modeled power source and a second sub-modelhaving a second modeled power source, wherein the validation score iscomputed for the first sub-model; computing an additional validationscore for the second sub-model, wherein the additional validation scoreexceeds the threshold validation score; and determining, based on theadditional validation score exceeding the threshold validation score,that a modeling error in the power network model is caused by the firstsub-model rather than the second sub-model, wherein the modification tothe power network model is applied to the first sub-model.
 14. Thesystem of claim 13, wherein the validation server is further configuredfor: outputting, by the validation server, a recommendation formodifying the power network model to resolve the modeling error; andapplying, by the validation server, the modification responsive toreceiving input indicating acceptance of the recommendation.
 15. Thesystem of claim 13, wherein the validation server is further configuredfor: identifying, by the validation server, the modification forresolving the modeling error; and automatically applying, by thevalidation server, the modification without receiving user inputindicative of selecting the modification.
 16. A method for verifyingthat a power network model matches an electric power network providingelectrical power to multiple assets positioned in multiple locations ina geographical area, the method comprising: accessing, by a validationdevice, the power network model, wherein the power network model isgenerated from data describing assets and power stations of the electricpower network; computing, by the validation device, a validation scorefor the power network model by performing operations comprising:computing a connectivity score indicating connectivity errors in thepower network model as compared to the electric power network, computingan asset score indicating power-delivery errors in the power networkmodel with respect to power-consuming assets serviced by the electricpower network, computing a power-flow score indicating power-flowcalculation errors in the power network model with respect to voltageranges specified for the electric power network, and calculating thevalidation score from the connectivity score, the asset score, and thepower-flow score; determining, by the validation device, that thevalidation score is below a threshold validation score; computing, bythe validation device, an updated validation score subsequent to amodification in the power network model, wherein the modificationchanges one or more of (i) connections in the power network model, (ii)power-delivery attributes in the power network model, and (iii)power-flow calculations in the power network model; and outputting, bythe validation device, the power network model with the modification toa control system for identifying and correcting errors in the electricpower network, wherein the power network model with the modification isoutputted based on the updated validation score exceeding the thresholdvalidation score.
 17. The method of claim 16, wherein computing theconnectivity score comprises: detecting connections among modeled nodesin the power network model and a modeled power source in the powernetwork model; generating, in a non-transitory computer-readable medium,a graph data structure representing the modeled nodes and theconnection; detecting, from the connections represented in the graphdata structure, de-energized nodes in the power network model that lackconnectivity to the modeled power source; and calculating theconnectivity score as a function of a number of the nodes and a numberof the de-energized nodes, wherein an increase in the number of thede-energized nodes causes the connectivity score to indicate increasedconnectivity errors, wherein the function of the number of the nodes andthe number of the de-energized nodes computes a percentage of the nodesthat have connectivity to the modeled power source.
 18. The method ofclaim 17, wherein computing the connectivity score further comprises andidentifying, from the power network model, unoccupied de-energized nodesindicating locations that are serviced by the electric power network andthat are modeled as lacking consumers, wherein the connectivity score isalso calculated from a number of unoccupied de-energized nodes, whereinan increase in the number of the unoccupied de-energized nodes causesthe connectivity score to indicate increased connectivity errors. 19.The method of claim 16, wherein computing the asset score comprises:identifying modeled assets included in the power network model;determining that deficient assets, which are a subset of the modeledassets, have power-delivery attribute values indicating a power-deliveryerror; and calculating the asset score as a function of a number of themodeled assets and a number of the deficient assets, wherein an increasein the number of the deficient assets causes the asset score to indicateincreased power-delivery errors.
 20. The method of claim 19, wherein thefunction of the number of the modeled assets and the number of thedeficient assets computes a percentage of the modeled assets that lackthe power-delivery errors.